Autonomous systems are used nowadays in more and more sectors from vehicles to domestic robots. They can make decisions on their own or interact with humans, thus their robustness and safety are properties of crucial importance. Due to the adaptive and context-aware nature of these systems, the testing of such properties is especially challenging. In this paper, we propose a model-based testing approach to capture the context and requirements of such systems, to automatically generate test data representing complex situations, and to evaluate test traces and compute test coverage metrics.